Binning disk drives during manufacturing by evaluating quality metrics prior to a final quality audit

ABSTRACT

A method of binning disk drives by evaluating quality metrics prior to a final quality audit is disclosed. A number of disk drives are assembled, and a plurality of quality metrics are generated for each disk drive representing a plurality of operating characteristics. The quality metrics for each disk drive are evaluated for binning the disk drives into a plurality of lots including a first lot and a second lot. A final quality audit (FQA) is performed by executing a number of write and read operations for a plurality of the disk drives in each lot and classifying each disk drive as passing or failing the FQA. If the number of disk drives that fail the FQA out of the first lot falls below a first threshold, the first lot is classified as acceptable for a first tier customer. If the number of disk drives that fail the FQA out of the second lot falls below a second threshold, the second lot is classified as acceptable for a second tier customer.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to disk drives for computer systems. Moreparticularly, the present invention relates to binning of disk drivesduring manufacturing by evaluating quality metrics prior to a FinalQuality Audit.

2. Description of the Prior Art

In disk drive manufacturing, drive failure prediction has in the pastbeen concerned with identifying and preventing marginal disk drives fromentering the market. A marginal disk drive may be discarded or reworkedby replacing defective components and then re-tested. The testingprocedure for detecting marginal disk drives (referred to as intelligentburn-in or IBI) involves vigorous operation of each disk drive over arange of temperature settings and testing conditions.

After the IBI process the disk drives are grouped into lots for shippingto particular customers. Prior to shipping, a final quality audit (FQA)is performed on a subset of disk drives within each lot. The FQAinvolves a subset of the test procedures performed during IBI and isused to help ensure the quality of disk drives within each lot satisfiesthe requirements of the customer. If the number of disk drives that failFQA exceeds a predetermined threshold, it is assumed that statisticallythe field failure rate of the disk drives in the lot will beunacceptable and the entire lot is rejected.

The field failure rate limit for disk drives may differ depending on thetarget market. For example, a first tier customer such as a manufactureof personal computers (PCs) may desire a very low field failure rate toestablish a reputation of high quality and reliability within the PCindustry. Accordingly PC manufactures are willing to pay a premium forhigher quality disk drives manufactured with higher quality components(e.g., higher quality heads, media, etc). In contrast, a second tiercustomer such as manufactures of personal video recorders (PVRs) maytolerate a higher field failure rate since the data stored on the diskdrives (i.e., video streams) is typically less sensitive to catastrophicloss as compared to user data stored in a PC. Tolerating a higher fieldfailure rate lowers the manufacturing cost of the PVR due to the pricebreak given by the disk drive manufactures.

Although manufacturing disk drives using higher quality componentsdecreases the number of marginal disk drives that fail FQA and thereforeincreases the manufacturing yield for first tier customers, theincreased cost of using higher quality components reduces the net profitto the disk drive manufacturer.

There is, therefore, a need to reduce the manufacturing cost of diskdrives while meeting the field failure rate requirements of both firstand second tier customers.

SUMMARY OF THE INVENTION

The present invention may be regarded as a method of manufacturing diskdrives. A number of disk drives are assembled, and a plurality ofquality metrics are generated for each disk drive wherein the qualitymetrics correspond to a plurality of operating characteristics. Thequality metrics for each disk drive are evaluated for binning the diskdrives into a plurality of lots including a first lot and a second lot.A final quality audit (FQA) is performed by executing a number of writeand read operations for a plurality of the disk drives in each lot andclassifying each disk drive as passing or failing the FQA. If the numberof disk drives that fail FQA out of the first lot falls below a firstthreshold, the first lot is classified as acceptable for a first tiercustomer. If the number of disk drives that fail the FQA out of thesecond lot falls below a second threshold, the second lot is classifiedas acceptable for a second tier customer.

In one embodiment, the first threshold is less than the secondthreshold. If the number of disk drives that fail the FQA out of thefirst lot falls between the first and second thresholds, the first lotis classified as acceptable for the second tier customer.

In another embodiment, the quality metrics for each disk drive arecombined into a composite score, wherein the step of evaluating thequality metrics of each disk drive comprises the step of evaluating thecomposite score. In one embodiment, the composite score is compared to abinning threshold. In one embodiment, the binning threshold is selectedrelative to the number of disk drives allocated to each lot. In anotherembodiment, the binning threshold is adapted during the binningoperation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a flow diagram according to an embodiment of the presentinvention wherein a binning score is generated for a plurality of diskdrives which is evaluated to bin the disk drives into respective lots.

FIG. 1B is a flow diagram according to an embodiment of the presentinvention wherein a binning threshold for binning the disk drive isadapted relative to the number of disk drives allocated to each lot.

FIG. 2 is a flow diagram according to an embodiment of the presentinvention wherein an adaptive drive failure prediction algorithm (DFPA)and a genetic algorithm are used to identify the subset of qualitymetrics and corresponding DFPA settings that are the best indicators ofdrive failure.

FIGS. 3A-3B show a flow diagram according to an embodiment of thepresent invention wherein a number of iterations are performed to adjustthe DFPA settings for each subset in a generation, and a number ofiterations are performed to evolve the subsets using the geneticalgorithm.

FIG. 4 illustrates an embodiment of the present invention wherein aplurality of genetic operators employed by the genetic algorithmincludes crossover, mutation, and replication.

FIG. 5A shows an embodiment of the present invention wherein theplurality of quality metrics is generated by computing secondary qualitymetrics from primary quality metrics according to a first and secondpredetermined functions.

FIG. 5B illustrates that in one embodiment the first and secondpredetermined functions are the logarithm base 10 and hyperbolic tangentfunctions.

FIG. 6A shows an embodiment of the present invention wherein the drivefailure prediction algorithm employs a neural network comprising aplurality of processing elements.

FIG. 6B shows an embodiment of each processing element in the neuralnetwork.

FIG. 7 illustrates the combined aspects of an embodiment of the presentinvention, including to select a subset of quality metrics using agenetic algorithm for input into a neural network implementing the DFPA.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1A is a flow diagram according to an embodiment of the presentinvention for manufacturing disk drives. At step 2 a number of diskdrives are assembled, and at step 4 a plurality of quality metrics aregenerated for each disk drive wherein the quality metrics correspond toa plurality of operating characteristics. The quality metrics for eachdisk drive are evaluated for binning the disk drives into a plurality oflots including a first lot and a second lot. At steps 6A and 6B a finalquality audit (FQA) is performed by executing a number of write and readoperations for a plurality of the disk drives in each lot andclassifying each disk drive at steps 8A and 8B as passing or failing theFQA. If at step 10A the number of disk drives that fail the FQA out ofthe first lot falls below a first threshold Th1, the first lot isclassified as acceptable for a first tier customer 12A. If at step 10Bthe number of disk drives that fail the FQA out of the second lot fallsbelow a second threshold Th2, the second lot is classified as acceptablefor a second tier customer 12B.

In one embodiment, a subset of disk drives within each lot are selectedfor FQA testing. If a disk drive fails FQA at steps 8A or 8B, the diskdrive is sent back to the disk drive assembly 2 for evaluation/reworkand a counter N_(f) is incremented at steps 14A or 14B. The FQA isexecuted for each disk drive in the subset until at steps 16A and 16Bthe entire subset of disk drives have been tested. In one embodiment,the first threshold Th1 is greater than the second threshold Th2 meaningthat the second tier customer 12B will accept a higher field failurerate than the first tier customer 12A. The first and second thresholdmay be set to any suitable value, and may be as low as one failure forthe first tier customer 12A. In other words, the entire lot of diskdrives may be rejected by the first tier customer 12A if a single drivefails the FQA 6A.

In the embodiment of FIG. 1A, a lot of disk drives that are consideredacceptable for a first tier customer may be allocated to a second tiercustomer if appropriate. For example, if at step 11 a sufficient numberof disk drives have already been allocated to the first tier customer12A, the lot is allocated to the second tier customer 12B. Anotheraspect illustrated in the embodiment of FIG. 1A is to “waterfall” a lotof disk drives that are rejected by the first tier customer 12A to thesecond tier customer 12B. That is, if at step 18 the number of diskdrives that fail the FQA 6A out of the first lot falls between the firstand second thresholds Th1 and Th2, the second lot is classified asacceptable for the second tier customer 12B. Otherwise the entire lot issent back to the disk drive assembly 2 for rework. Any suitable numberof tiered customers may be employed in the present invention wherein thelots of disk drives will waterfall down toward lower tier customersuntil finally being sent back to the assembly step 2 for rework if thelot is rejected by all of the tiered customers.

Binning the disk drives into respective lots based on a binning scorereduces the number of lots rejected by each of the tiered customers,thereby increasing the manufacturing yield and reducing manufacturingcost by reducing the number of lots returned to the disk drive assembly2 for rework. In one embodiment, the present invention obviates the needto use higher quality components in disk drives targeted for higher tiercustomers. In this embodiment, all of the disk drives are manufacturedwith the same quality of components and the disk drives that generate ahigher binning score at step 4 of FIG. 1A are grouped into the lotstargeted for higher tier customers. In other words, the disk driveshaving a lower tendency for field failure are grouped into the lots forthe higher tier customers which compensates for the need to manufacturethe disk drives with higher quality components. This reduces themanufacturing costs while still meeting the quality requirements ofhigher tier customers.

In one embodiment, a binning threshold used for binning the disk drivesinto respective lots is selected relative to the number of disk drivesallocated to each lot to ensure that the disk drives are distributed ina desired ratio amongst each tier customer. For example, if the binningthreshold is too high, not enough disk drives will be allocated to thelots for the first tier customer. In one embodiment illustrated in theflow diagram of FIG. 1B the binning threshold is continuously monitoredand adapted during the binning operation. At step 20 a number of qualitymetrics are generated for a disk drive, and at step 22 a binning scoreis generated based on the quality metrics. In this embodiment, thebinning score represents a probability that the disk drive will failFQA. If at step 24 the binning score is less than the binning threshold(BIN_TH) indicating a low probability of failure, then at step 26 thedisk drive is allocated to a first tier lot and at step 28 a counterTIER1 is incremented. If at step 24 the binning score is greater thanthe binning threshold indicating a higher probability of failure, thenat step 30 the disk drive is allocated to a second tier lot and at step32 a counter TIER2 is incremented. At step 34 or 36 the FQA is startedfor the disk drive and at step 38 a ratio of the counters TIER1/TIER2 isevaluated to adjust the binning threshold if needed.

Any suitable quality metrics may be employed in the present inventionfor generating the binning score at step 4 of FIG. 1A. Example qualitymetrics may include head/disk interface characteristics (e.g., head flyheight measurement, thermal asperity detection, etc.), read channelsettings (e.g., gain control, timing recovery, equalizer settings, etc),error correction parameters (e.g., number of retries, on-the-fly ECCerrors, off-line error correction, etc.), servo control parameters(e.g., seeking errors, tracking errors, etc.), and the like. Also, anysuitable algorithm may be employed at step 4 of FIG. 1A for evaluatingthe quality metrics to generate the binning score used for binning thedisk drives into their respective lots. In one embodiment, a geneticalgorithm is used to select a subset of the quality metrics that are thebest indicators of drive failure. A drive failure prediction algorithmis then used to evaluate the selected quality metrics to generate thebinning score.

FIG. 2 is a flow diagram illustrating the embodiment of the presentinvention wherein a genetic algorithm is used to select the subset ofquality metrics that are the best indicators of drive failure. At step42, a reference data base of quality metric values 44 and acorresponding failure indicator 46 is generated for a subset of diskdrives out of a family of disk drives. The reference data base may begenerated, for example, by evaluating the subset of disk drives using amanufacturing test that identifies failed disk drives as well as thecorresponding quality metrics. At step 48 an initial generation ofsubset quality metrics is selected from a group of M quality metrics 50,wherein each subset comprises N<M quality metrics and each qualitymetric can take on one of a number of quality metric values representinga quality of a disk drive. At step 52 a subset of quality metrics isselected from the generation, and at step 54 a drive failure predictionalgorithm (DFPA) is executed for the selected subset using the qualitymetric values 44 stored in the reference data base 42. At step 56 a meanabsolute error (MAE) is generated for the selected subset, wherein theMAE represents an accuracy of the drive failure prediction algorithmrelative to the failure indicators 46 stored in the reference data base42. At step 58, at least one setting of the DFPA is adjusted and theflow diagram starting at step 54 is repeated until a predeterminedcriteria is satisfied at step 60. Any suitable DFPA may be employed, andin an embodiment described below, the DFPA comprises a neural networkwherein the weights of the processing elements in the neural network areadjusted at step 58.

At step 62 a fitness score is generated for the selected subset inresponse to the current MAE, and the corresponding DFPA settings aresaved. If at step 64 there are more subsets to evaluate, the flowdiagram is repeated starting at step 52 by selecting another subset fromthe generation. Once a fitness score has been generated for each subset,at step 66 at least one genetic operator is applied to the subsets inresponse to the fitness scores to generate a new generation of subsets,wherein the genetic operator is selected from the group consisting of acrossover operator, a mutation operator, and a replication operator. Theflow diagram is then repeated starting at step 52 until a predeterminedcriteria is satisfied at step 68. At step 70 the subset of qualitymetrics that generated the best fitness score and the corresponding DFPAsettings for the subset are saved. The saved subset of quality metricsand DFPA settings are then used in the DFPA to predict failure of diskdrives in a manufacturing line or while in the field.

A genetic algorithm attempts to find a global maximum (best solution orbest fitness score) to a problem using Darwinian-type survival of thefittest type strategy whereby potential solutions to the problem competeand “mate” with each other in order to produce increasingly bettersolutions. In the context of finding the subset of quality metrics thatare the best indicators of drive failure, each subset of quality metricscan be considered as a chromosome wherein the quality metrics representsthe gene pool for the chromosomes. Comparing the output of the DFPA(step 56 of FIG. 2) to the failure indicators 46 in the reference database 42 is the means for measuring the fitness for each chromosome ofquality metrics. The genetic operators are then applied to thechromosomes having the highest fitness score (at step 66) to generate anew set of chromosomes that eventually “evolve” toward the optimalsolution.

FIGS. 3A-3B show a flow diagram according to an embodiment of thepresent invention wherein FIG. 3A shows a number of iterations areperformed to adjust the DFPA settings for each subset in a generationuntil the MAE falls below a threshold or a maximum number of iterationsis reached. FIG. 3B shows a minimum number of iterations are performedto evolve the subsets using the genetic algorithm. If after executingthe minimum number of iterations the fitness score is not trendingupward toward a new maximum, the genetic algorithm terminates. Thegenetic algorithm may also terminate if a maximum number of iterationsis reached.

Referring to FIG. 3A, at step 72 an iteration counter i and a variableBEST_(i) are initialized to zero, wherein the iteration counter i countsthe number of iterations for the genetic algorithm, and the variableBEST_(i) stores the best fitness score out of the subset of qualitymetrics in the current generation. At step 74 an iteration counter j isinitialized to zero, wherein the iteration counter j counts the numberof iterations for training the DFPA. The DFPA settings are alsoinitialized at step 74 (e.g., setting the weights of a neural network todefault values). At step 52 a subset of quality metric is selected fromthe current generation, and at step 76 the iteration counter j isincremented. At step 54 the DFPA algorithm is executed for the selectedsubset, and a corresponding MAE generated at step 56. If at step 78 theMAE is not less than a threshold and at step 82 the iteration counter jis less than a maximum, then at step 84 at least one DFPA setting isadjusted and the flow diagram is repeated starting at step 76. If atstep 78 the MAE is less than the threshold, or at step 82 the iterationcounter j is greater than the maximum, then at step 80 a fitness scoreis generated for the current subset in response to the MAE, and thecurrent DFPA settings are saved. The flow diagram of FIG. 3A is repeateduntil at step 88 a fitness score and DFPA settings have been saved foreach subset in the current generation, wherein flow control transfers toFIG. 3B.

At step 90 of FIG. 3B the current BEST_(i) fitness score is saved into avariable BEST_(i−1). At step 92 the best fitness score for a subset inthe generation (saved at step 80 of FIG. 3A) is selected and compared atstep 94 to BEST_(i). If the fitness score is greater than BEST_(i), thenat step 96 the fitness score is assigned to BEST_(i), and the selectedsubset is saved. If at step 98 there are more subsets to evaluate, thenthe flow diagram repeats starting with step 92. Once the best fitnessscore for each subset has been evaluated, at step 100 the iterationscounter i is incremented. If at step 102 the number of genetic evolutioniterations does not exceed a minimum, then at step 66 at least onegenetic operator is applied to the subsets in response to the bestfitness scores for the current subsets to generate a new generation ofsubsets and control transfers to step 74 of FIG. 3A to perform DFPAtraining on the new generation of subsets. If at step 102 the number ofgenetic evolution iterations exceeds the minimum, and at step 104 thecurrent BEST_(i) variable is not greater than the previous iterationBEST_(i−1) (saved at step 90), it indicates that the fitness score isnot trending toward a new maximum and the genetic algorithm thereforeterminates at step 106 after saving the subset and corresponding DFPAsettings that generated the best overall fitness score. The geneticalgorithm also terminates at step 106 if at step 108 the number ofgenetic evolution iterations has exceeded a maximum.

The process of genetic evolution is illustrated in FIG. 4 wherein aninitial generation of quality metric subsets (chromosomes) are selectedfrom an available pool of 25 quality metrics 110 numbered 1-25. In thisexample, two subsets 112A and 112B of four quality metrics are generatedby randomly selecting four quality metrics from the pool 110. The firstsubset 112A comprises quality metrics {04, 09, 14, 20} and the secondsubset 112B comprises quality metrics {07, 11, 23, 25}. Any suitablenumber of subsets of any suitable length may be generated to create theinitial generation of subsets; the example of FIG. 4 uses two subsets oflength four for the purpose of illustration. The DFPA is executed togenerate a fitness score for each subset. The crossover operator is thenapplied to the subsets 112A and 112B to generate a new generation ofsubsets 114A and 114B. In this example, the crossover operator isperformed on the last two quality metrics of subsets 112A and 112B, thatis, quality metrics {14, 20} of subset 112A are crossed over withquality metrics {23, 25} of subset 112B. The DFPA is then executed togenerate a fitness score for each of the new generation of subsets 114Aand 114B. The mutation operator is then applied to the subsets 114A and114B to generate a new generation of subsets 116A and 116B. In thisexample, the mutation operator mutates the second quality metric insubset 114A, that is, quality metric {09} in subset 114A is mutated to{12}. The DFPA is again executed to generate a fitness score for each ofthe new generation of subsets 116A and 116B. The replication operator isthen applied to the subsets 116A and 116B to generate a new generationof subsets 118A and 118B. In this example, the replication operatorreplicates the second subset 116B as the first subset 118B. The drivefailure prediction algorithm is again executed to generate a fitnessscore for each of the new generation of subsets 118A and 118B.

FIG. 5A shows an embodiment of the present invention wherein the groupof M quality metrics 50 (FIG. 2) comprises a primary set of qualitymetrics 120 and at least one predetermined function 122 operating on theprimary set of quality metrics 120 to generate a secondary set ofquality metrics 124. In an embodiment shown in FIG. 5B, thepredetermined function 122 comprises a logarithm base 10 function and ahyperbolic tangent function. Thus in FIG. 5A there are 25 qualitymetrics in the primary set 120 and 75 quality metrics in the secondaryset 124 after applying the logarithm base 10 function and a hyperbolictangent function on the primary set 120.

Any suitable DFPA may be employed in the embodiments of the presentinvention. FIG. 6A shows an embodiment wherein the DFPA employs a neuralnetwork 126 comprising an input layer, a hidden layer, and an outputlayer. Each layer comprises a number of processing elements (PE) whichare interconnected between the layers to form a directed graph. Each PEimplements any suitable function on the inputs. FIG. 6B shows anembodiment of a PE 128 wherein the function is simply the summation ofthe inputs Xi scaled by a respective weight Wi. In one embodiment theneural network is optimized (step 58 of FIG. 2) by adapting (i.e.,training) the weights Wi to improve the accuracy of the DFPA asdetermined from the reference data base 42 of quality metric values andcorresponding failure indicators 46. The output of the neural network isa composite score representing the propensity of drive failure based onthe combined influence of multiple quality metrics (four in theembodiments shown). The output of the neural network is evaluated atstep 56 of FIG. 2 to generate the fitness score used by the geneticalgorithm to select the subset of quality metrics that are the bestindicators of drive failure. The output of the neural network may alsobe used to predict drive failure during manufacturing (e.g., to generatethe binning score 4 in FIG. 1A) or while in-the-field.

FIG. 7 shows an overview of the embodiment of the present inventionusing a genetic algorithm to select the subset of quality metrics thatare the best indicators of drive failure. At least one function 122operates on the primary set of quality metrics 120 to generate asecondary set of quality metrics 124. The secondary set of qualitymetrics 124 are evaluated by the genetic algorithm 130 to select asubset of the quality metrics that are the best indicators of drivefailure. The selected subset of quality metrics are then input into aneural network 126, the output of which is a composite score used togenerate the fitness score for the genetic algorithm 130 and to predictdrive failure during manufacturing (e.g., to generate the binning score4 in FIG. 1A) or while in-the-field.

1. A method of manufacturing disk drives comprising the steps of: (a)assembling a number of disk drives; (b) generating a plurality ofquality metrics for each disk drive, wherein the quality metricscorrespond to a plurality of operating characteristics; (c) evaluatingthe quality metrics for each disk drive for binning the disk drives intoa plurality of lots including a first lot and a second lot; (d)performing a final quality audit (FQA) by executing a number of writeand read operations for a plurality of the disk drives in each lot andclassifying each disk drive as passing or failing the FQA; (e) if thenumber of disk drives out of the first lot that fail the FQA falls belowa first threshold, classifying the first lot as acceptable forpresenting to a first tier customer; and (f) if the number of diskdrives out of the second lot that fail the FQA falls below a secondthreshold, classifying the second lot as acceptable for presenting to asecond tier customer.
 2. The method of manufacturing disk drives asrecited in claim 1, wherein the first threshold is less than the secondthreshold.
 3. The method of manufacturing disk drives as recited inclaim 2, wherein if the number of disk drives that fail the FQA out ofthe first lot falls between the first and second thresholds, classifyingthe first lot as acceptable for presenting to the second tier customer.4. The method of manufacturing disk drives as recited in claim 1,further comprising the step of combining the quality metrics for eachdisk drive into a composite score representing an overall quality metricfor each disk drive, wherein the step of evaluating the quality metricsof each disk drive comprises the step of evaluating the composite score.5. The method of manufacturing disk drives as recited in claim 4,wherein the step of evaluating the composite score of each disk drivefor binning the disk drives comprises the step of comparing thecomposite score to a binning threshold.
 6. The method of manufacturingdisk drives as recited in claim 5, further comprising the step ofselecting the binning threshold relative to the number of disk drivesallocated to each lot.
 7. The method of manufacturing disk drives asrecited in claim 6, wherein the binning threshold is adapted during thebinning operation.